Leaders in smart manufacturing will be joining the virtual Predictive Analytics World (PAW) Industry 4.0 conference, as part of Machine Learning Week. Attendees will explore the latest trends and technologies in machine learning for the IoT era. The event will explore the impacts of these developing technologies from leaders in the field. Speakers include data scientists, industrial planners and other machine learning experts.
Steven Ramirez, CEO of Beyond the Arc, has been sharing his insights at PAW since its inception in 2009. He is honored to be the Program Chair for this year’s Industry 4.0 conference. Steven will focus the 2020 conference theme on how machine learning and predictive analytics enable IoT devices to gather and analyze data to improve productivity, efficiency, and service reliability.
This year’s conference includes the following notable speakers in IoT, smart manufacturing, and operational excellence:
A. Charles Thomas – Chief Data & Analytics Officer at GM
“Becoming Data Driven in the Automotive Industry”
Drawing from his experience as the chief data and analytics officer at three different companies, A. Charles Thomas – now chief data and analytics officer at General Motors – will share insights and lessons learned from both sides of the unique, two-pronged role he plays at GM.
First, Charles’ team leverages analytics to enhance GM’s traditional businesses, such as selling vehicles, OnStar, Warranty, SiriusXM, and others. The team generates insights to drive billion-dollar improvements in functions such as manufacturing, HR, Marketing, and Digital.
Second, Charles’ team also drives revenue from their unique access to tremendous quantities of vehicle data. This includes direct licensing of connected vehicle data (e.g. GPS data to traffic and parking apps, media, retail, and insurance companies), as well as using these data to create new businesses in insurance, fleet management, and others.
In this keynote address, Charles will share his unique insider’s vantage.
Gil Arditi – Product Lead, Machine Learning at Lyft
“From Self-Driving to Fraud Detection – How Lyft Streamlines Machine Learning Deployment”
In this keynote address, Gil Arditi will cover the areas of machine learning development at Lyft, talk about friction points in the model lifecycle – from prototyping and feature engineering to production deployment – and show how Lyft streamlined this process internally. He will also cover a step-by-step example of a model that was recently developed and taken to production.
Steven Ramirez – CEO, at Beyond the Arc
“Kiss Your Analytics Software Suppliers Goodbye? Opportunities and Pitfalls in Open Source”
Open source software has become popular for machine learning tasks of all kinds. In this presentation, Steven will weigh in on the pros and cons. While open source software is free to download and install, it doesn’t mean that the Total Cost of Ownership (TCO) is zero. In this keynote he’ll also provide an overview of packages available for Python to build and deploy machine learning, natural language processing, computer vision, and other advanced models.
Terry Miller – Global Digital Strategy and Business Development at Siemens
“Industrial Asset Optimization: Machine-to-Cloud/Edge Analytics”
In this keynote, Terry Miller, from Siemens, will evaluate a case study utilizing this architecture to capture and predict valve “stiction” in a Wastewater treatment plant flow loop. He emphasizes how biggest impediments to Industrial firms realizing the efficiencies promised by “Industry 4.0” remains the access to quality, (near) real-time data. Ignoring the Purdue model, smart manufacturing firms should utilize cutting-edge cyber security tools to connect critical assets directly with professionals having the skills necessary to deploy advanced analytics solutions, optimizing machines and processes.
Jaya Mathew – Senior Data Scientist at Microsoft
“Getting the Most out of Your IoT Data: Basics of Predictive Maintenance, Bootstrapping your Model with Your Data & Comparing the Traditional vs Deep Learning Methodologies”
In her keynote address, Jaya Mathew emphasizes how organizations are routinely faced with the challenge of how to analyze their IoT data. This talk will focus on companies who collect data from their factory operations and are interested in predicting mechanical failures. The audience will get an overview of the entire process starting with how to formulate their business problem, perform feature engineering and build a predictive maintenance model using Python using both tradition/Deep learning techniques.
Andrei Khurshudov – Director, Advanced Analytics at Caterpillar Digital
“Predictive Analytics for the Internet of Things”
Andrei Khurshudov specializes in Big Data Analytics, the Internet of Things, Cloud storage and computing, in-memory computing, and data storage reliability and technology. His keynote will look to the future as he focuses on the rare opportunity for us to live in a time in which important new fields as Big Data Analytics (BDA) and the Internet of Things (IoT) are being born, maturing, and working together to advance technological progress. The presentation will outline new opportunities and challenges for predictive analytics when applied to the field of Industrial IoT. It will also discuss various drivers and inhibitors that need to be considered, as well as successful strategies for offering predictive analytics for IoT.
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Streamline manufacturing, IoT, and supply chain with machine learning. The conversation takes place at Predictive Analytics World for Industry 4.0, a part of Machine Learning Week. #pawcon #iot #machinelearning #AI @pawcon @beyondthearc
Come to the virtual edition of Predictive Analytics World Industry 4.0 and gain access to the best keynotes, workshops, smart manufacturing enterprise leaders and industry heavyweights in the business.
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